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Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model.

J Yogapriya1, Venkatesan Chandran2, M G Sumithra2,3

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This study introduces an AI model using deep convolutional neural networks (CNNs) to automatically detect gastrointestinal diseases from wireless capsule endoscopy images, achieving 96.33% accuracy with the VGG16 model.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Wireless capsule endoscopy generates vast amounts of data, requiring extensive manual analysis by medical professionals.
  • Automating the analysis of wireless capsule endoscopy images is crucial for efficient diagnosis of gastrointestinal diseases.
  • Existing methods for disease classification from endoscopic images often require significant manual intervention or lack sufficient accuracy.

Purpose of the Study:

  • To develop and evaluate a deep convolutional neural network (CNN) model for automated classification of gastrointestinal diseases using wireless capsule endoscopy images.
  • To compare the performance of different pretrained CNN architectures (VGG16, ResNet-18, GoogLeNet) for this specific task.
  • To improve the efficiency and accuracy of diagnosing gastrointestinal conditions through image analysis.

Main Methods:

  • A hybrid approach combining traditional image processing algorithms, data augmentation, and adjusted pretrained CNN models (VGG16, ResNet-18, GoogLeNet).
  • The CNN models featured adjusted fully connected and output layers tailored for the classification task.
  • Validation was performed on a dataset comprising 6702 wireless capsule endoscopy images across 8 distinct disease classes.

Main Results:

  • The VGG16 model demonstrated superior performance, achieving an accuracy of 96.33%, recall of 96.37%, precision of 96.5%, and F1-measure of 96.5%.
  • The VGG16 model attained the highest Matthews Correlation Coefficient (0.95) and Cohen's kappa score (0.96) among the evaluated models.
  • All proposed models showed promising results in classifying gastrointestinal tract diseases from endoscopic images.

Conclusions:

  • The developed VGG16-based CNN model effectively classifies gastrointestinal diseases from wireless capsule endoscopy images with high accuracy.
  • This AI-driven approach offers a significant improvement over manual analysis, potentially reducing diagnostic time and enhancing patient care.
  • The study highlights the potential of deep learning in revolutionizing the interpretation of medical imaging data in gastroenterology.